



'''simple docstring'''



import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def        UpperCAmelCase (    A	:					Optional[int]					):
							SCREAMING_SNAKE_CASE       :   str         =      SwinConfig()
							SCREAMING_SNAKE_CASE       :   int         =      swin_name.split('''_'''					)

							SCREAMING_SNAKE_CASE       :   int         =      name_split[1]
							SCREAMING_SNAKE_CASE       :   List[Any]         =      int(name_split[4]					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      int(name_split[3][-1]					)

							if model_size == "tiny":
														SCREAMING_SNAKE_CASE       :   str         =      96
														SCREAMING_SNAKE_CASE       :   List[str]         =      (2, 2, 6, 2)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      (3, 6, 12, 24)
							elif model_size == "small":
														SCREAMING_SNAKE_CASE       :   List[Any]         =      96
														SCREAMING_SNAKE_CASE       :   Any         =      (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      (3, 6, 12, 24)
							elif model_size == "base":
														SCREAMING_SNAKE_CASE       :   List[str]         =      128
														SCREAMING_SNAKE_CASE       :   List[str]         =      (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE       :   Tuple         =      (4, 8, 16, 32)
							else:
														SCREAMING_SNAKE_CASE       :   Dict         =      192
														SCREAMING_SNAKE_CASE       :   Tuple         =      (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      (6, 12, 24, 48)

							if "in22k" in swin_name:
														SCREAMING_SNAKE_CASE       :   Tuple         =      21841
							else:
														SCREAMING_SNAKE_CASE       :   Tuple         =      1000
														SCREAMING_SNAKE_CASE       :   str         =      '''huggingface/label-files'''
														SCREAMING_SNAKE_CASE       :   str         =      '''imagenet-1k-id2label.json'''
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      json.load(open(hf_hub_download(A       ,		A       ,		repo_type='''dataset'''					)       ,		'''r'''					)					)
														SCREAMING_SNAKE_CASE       :   List[str]         =      {int(A					): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      idalabel
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE       :   List[str]         =      img_size
							SCREAMING_SNAKE_CASE       :   str         =      num_classes
							SCREAMING_SNAKE_CASE       :   List[str]         =      embed_dim
							SCREAMING_SNAKE_CASE       :   Tuple         =      depths
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      num_heads
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      window_size

							return config
def        UpperCAmelCase (    A	:					Dict					):
							if "patch_embed.proj" in name:
														SCREAMING_SNAKE_CASE       :   Dict         =      name.replace('''patch_embed.proj'''       ,		'''embeddings.patch_embeddings.projection'''					)
							if "patch_embed.norm" in name:
														SCREAMING_SNAKE_CASE       :   List[str]         =      name.replace('''patch_embed.norm'''       ,		'''embeddings.norm'''					)
							if "layers" in name:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''encoder.''' + name
							if "attn.proj" in name:
														SCREAMING_SNAKE_CASE       :   Any         =      name.replace('''attn.proj'''       ,		'''attention.output.dense'''					)
							if "attn" in name:
														SCREAMING_SNAKE_CASE       :   Any         =      name.replace('''attn'''       ,		'''attention.self'''					)
							if "norm1" in name:
														SCREAMING_SNAKE_CASE       :   Tuple         =      name.replace('''norm1'''       ,		'''layernorm_before'''					)
							if "norm2" in name:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      name.replace('''norm2'''       ,		'''layernorm_after'''					)
							if "mlp.fc1" in name:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      name.replace('''mlp.fc1'''       ,		'''intermediate.dense'''					)
							if "mlp.fc2" in name:
														SCREAMING_SNAKE_CASE       :   Tuple         =      name.replace('''mlp.fc2'''       ,		'''output.dense'''					)

							if name == "norm.weight":
														SCREAMING_SNAKE_CASE       :   int         =      '''layernorm.weight'''
							if name == "norm.bias":
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''layernorm.bias'''

							if "head" in name:
														SCREAMING_SNAKE_CASE       :   int         =      name.replace('''head'''       ,		'''classifier'''					)
							else:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''swin.''' + name

							return name
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					Any					):
							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE       :   List[str]         =      orig_state_dict.pop(A					)

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					SCREAMING_SNAKE_CASE       :   int         =      key.split('''.'''					)
																					SCREAMING_SNAKE_CASE       :   Dict         =      int(key_split[1]					)
																					SCREAMING_SNAKE_CASE       :   Dict         =      int(key_split[3]					)
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												SCREAMING_SNAKE_CASE       :   Dict         =      val[:dim, :]
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      val[
																												    dim : dim * 2, :
																												]
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      val[-dim:, :]
																					else:
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      val[
																												    :dim
																												]
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      val[
																												    dim : dim * 2
																												]
																												SCREAMING_SNAKE_CASE       :   List[Any]         =      val[
																												    -dim:
																												]
														else:
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      val

							return orig_state_dict
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					str					):
							SCREAMING_SNAKE_CASE       :   List[Any]         =      timm.create_model(A       ,		pretrained=A					)
							timm_model.eval()

							SCREAMING_SNAKE_CASE       :   int         =      get_swin_config(A					)
							SCREAMING_SNAKE_CASE       :   List[str]         =      SwinForImageClassification(A					)
							model.eval()

							SCREAMING_SNAKE_CASE       :   int         =      convert_state_dict(timm_model.state_dict()       ,		A					)
							model.load_state_dict(A					)

							SCREAMING_SNAKE_CASE       :   Tuple         =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

							SCREAMING_SNAKE_CASE       :   List[Any]         =      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''       ,		'''-'''					)					)					)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      Image.open(requests.get(A       ,		stream=A					).raw					)
							SCREAMING_SNAKE_CASE       :   int         =      image_processor(images=A       ,		return_tensors='''pt'''					)

							SCREAMING_SNAKE_CASE       :   str         =      timm_model(inputs['''pixel_values''']					)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      model(**A					).logits

							assert torch.allclose(A       ,		A       ,		atol=1e-3					)

							print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""					)
							model.save_pretrained(A					)

							print(F"""Saving image processor to {pytorch_dump_folder_path}"""					)
							image_processor.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Any             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--swin_name',
							    default='swin_tiny_patch4_window7_224',
							    type=str,
							    help='Name of the Swin timm model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)

							lowerCAmelCase_  :							Optional[Any]             =    parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np
def        UpperCAmelCase (    A	:					dict       ,		A	:					str       ,		A	:					set       ,		A	:					set       ,		A	:					dict       ,		A	:					dict       ,		A	:					PriorityQueue       ,		A	:					dict       ,		A	:					float | int       ,		):
							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE       :   str         =      cst_fwd.get(A       ,		np.inf					)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      new_cost_f
																					SCREAMING_SNAKE_CASE       :   List[str]         =      v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					dict       ,		A	:					dict					):
							SCREAMING_SNAKE_CASE       :   Dict         =      -1

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      set()
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      set()
							SCREAMING_SNAKE_CASE       :   Any         =      {source: 0}
							SCREAMING_SNAKE_CASE       :   Tuple         =      {destination: 0}
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {source: None}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      {destination: None}
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()

							SCREAMING_SNAKE_CASE       :   List[str]         =      np.inf

							queue_forward.put((0, source)					)
							queue_backward.put((0, destination)					)

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      queue_forward.get()
														visited_forward.add(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      queue_backward.get()
														visited_backward.add(A					)

														SCREAMING_SNAKE_CASE       :   Tuple         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE       :   int         =      shortest_distance
							return shortest_path_distance


lowerCAmelCase_  :							int             =    {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCAmelCase_  :							str             =    {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
							import doctest

							doctest.testmod()




